Add clean up and fixes on top of #2361:

• [x] Fix l2 of all models
• [x] Fix perplexity of the sklearn NMF
• [x] Change loss function in the logs of the model
• [x] Print topics in the wikipedia notebook
• [x] Reorder the benchmark tables
• [x] Fix an out-of-bounds error in grouper
• [x] Specify the shape of the corpus in the module docstring
• [x] Fix the generator issue
• [x] Add more unittests
• [x] More explanations in wikipedia
• [x] Fix flake8
• [x] Add comment about sparsity method
• [x] Mention pass parameter behavior for generators/iterators
• [x] Fix smart_open import
• [x] Fix tests

Tutorial explaining the whats & hows: Jupyter notebook

note: all preliminary discussions are in https://github.com/RaRe-Technologies/gensim/pull/2048

This PR summarizes all my work during GSoC 2018. For more understanding what's going on, follow the links:

• My proposal: https://persiyanov.github.io/jekyll/update/2018/04/24/accepted-to-gsoc-2018.html
• First benchmarks: https://persiyanov.github.io/jekyll/update/2018/05/28/gsoc-first-weeks.html
• Last blog post about the almost final solution: https://persiyanov.github.io/2018/07/06/gsoc-midreport.html
• Links to all benchmarks: https://gist.github.com/persiyanov/84b806233947e0069a243433579b35db
• Previous PR about vocab building : https://github.com/RaRe-Technologies/gensim/pull/2078 (reverted these changes in current PR because of API design issues)
• Previous PR about multistream training (all useful changes in this PR): https://github.com/RaRe-Technologies/gensim/pull/2048

Summary

In this pull request, new argument corpus_file is proposed for Word2Vec, FastText and Doc2Vec models. It is supposed to use corpus_file instead of standard sentences argument if you have the preprocessed dataset on disk and want to get significant speedup during model training.

On our benchmarks, training Word2Vec on English Wikipedia dump is 370% faster with corpus_file than training with sentences (see the attached jupyter notebook with the code).

Look at this chart for Word2Vec:

Usage

The usage is really simple. I'll provide examples for Word2Vec while the usage for FastText and Doc2Vec is identical. The corpus_file argument is supported for:

Constructor

# Standard way
model = Word2Vec(sentences=my_corpus, <...other arguments...>)

# New way
model = Word2Vec(corpus_file='my_corpus_saved.txt', <...other arguments...>)

# You can save your own corpus using
gensim.utils.save_as_line_sentence(my_corpus, 'my_corpus_saved.txt')

build_vocab

# Create the model without training
model = Word2Vec(<...other arguments...>)

# Standard way
model.build_vocab(sentences=my_corpus, ...)

# New way
model.build_vocab(corpus_file='my_corpus_saved.txt', ...)

train

# Create the model without training
model = Word2Vec(<...other arguments...>)

# Build vocab (with `sentences` or `corpus_file` way, choose what you like)
model.build_vocab(corpus_file='my_corpus_saved.txt')

# Train the model (old way)
model.train(sentences=my_corpus, total_examples=model.corpus_count, ...)

# Train the model (new way)
model.train(corpus_file='my_corpus_saved.txt', total_words=model.corpus_total_words, ...)

That's it! Everything else remains the same as before.

Details

Firstly, let me describe the standard approach to train *2Vec models:

1. A user provides input data stream (python iterable object)
2. One job_producer python thread is created. This thread reads data from the input stream and pushes batches into the python threading.Queue (job_queue).
3. Several worker threads pull batches from job_queue and perform model updates. Batches are python lists of lists of tokens. They are first translated into C structures and then a model update is performed without GIL.

Such approach allows to scale model updates linearly, but batch producing (from reading up to filling C structures from python object) is a bottleneck in this pipeline.

It is evident that we can't optimize batch generation for abstract python stream (with custom user logic). Instead of this, we performed such an optimization only for data which is stored on a disk in a form of gensim.models.word2vec.LineSentence (one sentence per line, words are separated by whitespace).

Such a restriction allowed us to read the data directly on C++ level without GIL. And then, immediately, perform model updates. Finally, this resulted in linear scaling during training.

Introduction

The LevenshteinSimilarityIndex term similarity index in the termsim.levenshtein module implements the lexical text similarity search technique described by Charlet and Damnati (2017) in their paper describing their winning system at SemEval-2017 Task 3: Community Question Answering.

We are showing a related semantic similarity search technique using the WordEmbeddingSimilarityIndex term similarity index in our Soft Cosine Similarity autoexample, which enjoys some popularity among our users. We would like to also advertise LevenshteinSimilarityIndex, which provides a different but equally useful kind of search. However, the current implementation uses brute-force kNN search over the Levenshtein distance to produce a term similarity matrix, which is so slow that it can take years to produce a matrix even for medium-sized corpora such as the English Wikipedia.

Following the discussion in https://github.com/RaRe-Technologies/gensim/issues/2541, @piskvorky and I implemented indexing using the FastSS algorithm for kNN search over the Levenshtein distance in hopes that this would speed LevenshteinSimilarityIndex up by at least three orders of magnitude (1,000×), so that it can compete with WordEmbeddingSimilarityIndex. As an added bonus, using the FastSS algorithm allows us to remove our external dependence on the python-Levenshtein library.

Speed comparison

Below, I will show a before-and-after speed comparison of LevenshteinSimilarityIndex compared to the standard WordEmbeddingSimilarityIndex shown in the Soft Cosine Similarity autoexample. We are measuring how many kNN searches per second, k = 100, a term similarity index can perform. To produce my dictionary (253,854 terms) and word embeddings, I will use the text8 corpus (100 MB). I am running the code on a Dell Inspiron 15 7559.

Before the change

We can see that even with our tiny corpus, the LevenshteinSimilarityIndex takes over three days to find the 100 nearest neighbors for all 253,854 terms in our vocabulary. Contrast this with the WordEmbeddingSimilarityIndex, which finishes in under four minutes even though we are using exact nearest-neighbor search and we could get further speed-up by using e.g. the Annoy index.

$ pip install gensim==4.0.1 python-Levenshtein
$ wget http://mattmahoney.net/dc/text8.zip
$ unzip text8.zip
$ python
>>> from gensim.corpora import Dictionary
>>> from gensim.models.word2vec import LineSentence, Word2Vec
>>> from gensim.similarities import (
...     SparseTermSimilarityMatrix,
...     WordEmbeddingSimilarityIndex,
...     LevenshteinSimilarityIndex,
... )
>>> 
>>> corpus = LineSentence('text8')
>>> dictionary = Dictionary(corpus)
>>> w2v_model = Word2Vec(sentences=corpus)
>>> embedding_index = WordEmbeddingSimilarityIndex(w2v_model.wv)
>>> levenshtein_index = LevenshteinSimilarityIndex(dictionary)
>>>
>>> SparseTermSimilarityMatrix(embedding_index, dictionary)
100%|███████████████████████████████| 253854/253854 [04:04<00:00, 1037.97it/s]
>>> SparseTermSimilarityMatrix(levenshtein_index, dictionary)
  0%|                               | 124/253854 [02:24<80:18:05,  1.14s/it]

After the change

With the FastSS algorithm, the LevenshteinSimilarityIndex receives a 1,500× speed-up and is now not only not slower than the WordEmbeddingSimilarityIndex, but 1.5× faster. Both term similarity indexes now find the 100 nearest neighbors for all 253,854 terms in our vocabulary in under 4 minutes.

$ pip install lexpy git+https://github.com/witiko/[email protected]
$ python
>>> from gensim.corpora import Dictionary
>>> from gensim.models.word2vec import LineSentence, Word2Vec
>>> from gensim.similarities import (
...     SparseTermSimilarityMatrix,
...     WordEmbeddingSimilarityIndex,
...     LevenshteinSimilarityIndex,
... )
>>> 
>>> corpus = LineSentence('text8')
>>> dictionary = Dictionary(corpus)
>>> w2v_model = Word2Vec(sentences=corpus)
>>> embedding_index = WordEmbeddingSimilarityIndex(w2v_model.wv)
>>> levenshtein_index = LevenshteinSimilarityIndex(dictionary)
>>>
>>> SparseTermSimilarityMatrix(embedding_index, dictionary)
100%|███████████████████████████████| 253854/253854 [03:57<00:00, 1070.14it/s]
>>> SparseTermSimilarityMatrix(levenshtein_index, dictionary)
100%|███████████████████████████████| 253854/253854 [02:34<00:00, 1639.23it/s]

Conclusion

Using the FastSS algorithm for kNN search over the Levenshtein distance, we managed to increase the speed of the LevenshteinSimilarityIndex term similarity index by four orders of magnitude (1,500×) on the text8 corpus. As an added bonus, using the FastSS algorithm allowed us to remove our external dependence on the Levenshtein library. Closes #2541.

Problem description

When importing gensim I get the following error

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/mbertoni/software/miniconda3/envs/test/lib/python3.7/site-packages/gensim/__init__.py", line 11, in <module>
    from gensim import parsing, corpora, matutils, interfaces, models, similarities, utils  # noqa:F401
  File "/home/mbertoni/software/miniconda3/envs/test/lib/python3.7/site-packages/gensim/corpora/__init__.py", line 6, in <module>
    from .indexedcorpus import IndexedCorpus  # noqa:F401 must appear before the other classes
  File "/home/mbertoni/software/miniconda3/envs/test/lib/python3.7/site-packages/gensim/corpora/indexedcorpus.py", line 14, in <module>
    from gensim import interfaces, utils
  File "/home/mbertoni/software/miniconda3/envs/test/lib/python3.7/site-packages/gensim/interfaces.py", line 19, in <module>
    from gensim import utils, matutils
  File "/home/mbertoni/software/miniconda3/envs/test/lib/python3.7/site-packages/gensim/matutils.py", line 1024, in <module>
    from gensim._matutils import logsumexp, mean_absolute_difference, dirichlet_expectation
  File "gensim/_matutils.pyx", line 1, in init gensim._matutils
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

Steps/code/corpus to reproduce

conda create --name=test python=3.7 -y
conda install -y numpy==1.19.2
pip install gensim

Versions

Linux-5.11.0-25-generic-x86_64-with-debian-bullseye-sid Python 3.7.11 (default, Jul 27 2021, 14:32:16) [GCC 7.5.0] Bits 64 NumPy 1.19.2 SciPy 1.7.1

Doc2vec does not use all my cores despite my setting workers=8 when I instantiate it.

My install passes the assert below:

assert gensim.models.doc2vec.FAST_VERSION > -1

Do I have to do something else?

Description

When building an LsiModel in distributed mode, one of the workers gets "stuck" while orthonormalizing the action matrix. This stalls the whole process of building the model, as the dispatcher hangs on "reached the end of input; now waiting for all remaining jobs to finish".

Steps/Code/Corpus to Reproduce

lsi_model = LsiModel(
        id2word=bow,
        num_topics=300,
        chunksize=5000,
        distributed=True
    )
lsi_model.add_documents(corpus)

LSI dispatcher and workers are initialized in separate bash script. I have tried with the number of LSI workers set to 16 and 8.

Gensim version: 3.6.0 Pyro4 version: 4.63

Expected Results

Process should run to completion

Actual Results

Main script output:

[2019-01-06 04:04:09,862] [23465] [gensim.models.lsimodel] [INFO] {add_documents:462} updating model with new documents
[2019-01-06 04:04:09,862] [23465] [gensim.models.lsimodel] [INFO] {add_documents:485} initializing 8 workers
[2019-01-06 04:05:12,131] [23465] [gensim.models.lsimodel] [INFO] {add_documents:488} preparing a new chunk of documents
[2019-01-06 04:05:12,135] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:492} converting corpus to csc format
[2019-01-06 04:05:12,497] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:499} creating job #0
[2019-01-06 04:05:12,541] [23465] [gensim.models.lsimodel] [INFO] {add_documents:503} dispatched documents up to #5000
[2019-01-06 04:06:46,191] [23465] [gensim.models.lsimodel] [INFO] {add_documents:488} preparing a new chunk of documents
[2019-01-06 04:06:46,200] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:492} converting corpus to csc format
[2019-01-06 04:06:46,618] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:499} creating job #1
[2019-01-06 04:06:46,682] [23465] [gensim.models.lsimodel] [INFO] {add_documents:503} dispatched documents up to #10000
[2019-01-06 04:08:11,839] [23465] [gensim.models.lsimodel] [INFO] {add_documents:488} preparing a new chunk of documents
[2019-01-06 04:08:11,843] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:492} converting corpus to csc format
[2019-01-06 04:08:12,561] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:499} creating job #2
[2019-01-06 04:08:12,786] [23465] [gensim.models.lsimodel] [INFO] {add_documents:503} dispatched documents up to #15000
[2019-01-06 04:09:48,217] [23465] [gensim.models.lsimodel] [INFO] {add_documents:488} preparing a new chunk of documents
[2019-01-06 04:09:48,230] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:492} converting corpus to csc format
[2019-01-06 04:09:48,700] [23465] [gensim.models.lsimodel] [DEBUG] {add_documents:499} creating job #3
[2019-01-06 04:09:48,786] [23465] [gensim.models.lsimodel] [INFO] {add_documents:503} dispatched documents up to #20000
[2019-01-06 04:09:48,938] [23465] [gensim.models.lsimodel] [INFO] {add_documents:518} reached the end of input; now waiting for all remaining jobs to finish

Output of LSI worker that is stuck:

2019-01-06 04:04:09,867 - INFO - resetting worker #1
2019-01-06 04:06:46,705 - INFO - worker #1 received job #208
2019-01-06 04:06:46,705 - INFO - updating model with new documents
2019-01-06 04:06:46,705 - INFO - using 100 extra samples and 2 power iterations
2019-01-06 04:06:46,705 - INFO - 1st phase: constructing (500000, 400) action matrix
2019-01-06 04:06:48,402 - INFO - orthonormalizing (500000, 400) action matrix

CPU for that LSI worker has been ~100% for >24 hours.

Versions

Linux-4.10.0-38-generic-x86_64-with-Ubuntu-16.04-xenial Python 3.5.2 (default, Nov 23 2017, 16:37:01) [GCC 5.4.0 20160609] NumPy 1.15.2 SciPy 1.1.0 gensim 3.6.0 FAST_VERSION 1

Facebook's recent open sourced fasttext https://github.com/facebookresearch/fastText improves the word2vec SkipGram model. It follows a similar output format for word - vector key value pairs, and the similarity calculation is about the same too, but their binary output format is kind of different from that of the C version of word2vec binary format. Do we want to support loading fastText model output in gensim? Thanks.

Hi, I added nmslib indexer.

Some research shows nmslib is better than annoy indexer. https://erikbern.com/2018/06/17/new-approximate-nearest-neighbor-benchmarks.html https://www.benfrederickson.com/approximate-nearest-neighbours-for-recommender-systems/

This is the first time to contribute to gensim. If I miss something, please let me know.

A reasonable approximation of the method described in the paper Distributed Representations of Sentences and Documents (http://cs.stanford.edu/~quocle/paragraph_vector.pdf).

Python isn't my first language, so I don't pretend that I did everything in the most "pythonic" way here, but I pulled out the portions of the word2vec code that needed to be modified into their own methods, then added another class extending word2vec which just modifies those few functions.

I also don't really know what to do in terms of refactoring cython code to limit code duplication, so doc2vec has its own set of cython functions which are completely independent of the word2vec ones.

Hope that helps. Tim

word2vec embeddings start with a line with the number of lines (tokens?) and the number of dimensions of the file. This allows gensim to allocate memory accordingly for querying the model. Larger dimensions mean larger memory is held captive. Accordingly, this line has to be inserted into the GloVe embeddings file.

This pull request implements the gensim.models.bm25model module, which contains an implementation of the Okapi BM25 model and its modifications (Lucene BM25 and ATIRE) as discussed in https://github.com/RaRe-Technologies/gensim/issues/2592#issuecomment-866799145. The module acts as a replacement for the gensim.summarization.bm25model module deprecated and removed in Gensim 4. The module should supersede the gensim.models.tfidfmodel module as the baseline weighting function for information retrieval and related NLP tasks.

Most implementations of BM25 such as the rank-bm25 library combine indexing with weighting and often forgo dictionary building for a speed improvement at indexing time (but a hefty penalty at retrieval time). To give an example, here is how a user would search for documents with rank-bm25:

>>> from rank_bm25 import BM25Okapi
>>>
>>> corpus = [["Hello", "world"], ["bar", "bar"], ["foo", "bar"]]
>>> bm25_model = BM25Okapi(corpus)
>>>
>>> query = ["Hello", "bar"]
>>> similarities = bm25_model.get_scores(query)
>>> similarities

array([0.51082562, 0.09121886, 0.0638532 ])

>>> best_document, = bm25_model.get_top_n(query, corpus, n=1)
>>> best_document

['Hello', 'world']

As you can see, the interface is convenient, but retrieval is slow due to the lack of a dictionary. Furthermore, any advanced operations such as pruning the dictionary, applying semantic matching (e.g. SCM) and query expansion (e.g. RM3), or sharding the index are unavailable.

By contrast, the gensim.models.bm25 module separates the three operations. To give an example, here is how a user would search for documents with the gensim.models.bm25 module:

>>> from gensim.corpora import Dictionary
>>> from gensim.models import TfidfModel, OkapiBM25Model
>>> from gensim.similarities import SparseMatrixSimilarity
>>> import numpy as np
>>>
>>> corpus = [["Hello", "world"], ["bar", "bar"], ["foo", "bar"]]
>>> dictionary = Dictionary(corpus)
>>> bm25_model = OkapiBM25Model(dictionary=dictionary)
>>> bm25_corpus = bm25_model[list(map(dictionary.doc2bow, corpus))]
>>> bm25_index = SparseMatrixSimilarity(bm25_corpus, num_docs=len(corpus), num_terms=len(dictionary),
...                                     normalize_queries=False, normalize_documents=False)
>>>
>>> query = ["Hello", "bar"]
>>> tfidf_model = TfidfModel(dictionary=dictionary, smartirs='bnn')  # Enforce binary weighting of queries
>>> tfidf_query = tfidf_model[dictionary.doc2bow(query)]
>>>
>>> similarities = bm25_index[tfidf_query]
>>> similarities

array([0.51082563, 0.09121886, 0.0638532 ], dtype=float32)

>>> best_document = corpus[np.argmax(similarities)]
>>> best_document

['Hello', 'world']

Tasks:

• [x] Add Okapi BM25, ~~BM25L and BM25⁺~~ [1, 2], Lucene BM25 [3, 4], and ATIRE BM25 [3, 5].
• [x] Add comments and docstrings to models.bm25.
• [x] Add comments and docstrings to similarities.docsim.
• [x] Add BM25 to the run_topics_and_transformations autoexample.
• [x] Add normalize_queries=True, normalize_documents=True named parameters to SparseMatrixSimilarity, DenseMatrixSimilarity, and SoftCosineSimilarity classes as discussed in https://github.com/RaRe-Technologies/gensim/pull/3304#issuecomment-1061031969 and on the Gensim mailing list. Deprecate the normalize named parameter of SoftCosineSimilarity. Add normalize_queries=False, normalize_documents=False to TF-IDF and BM25 examples.

When installing gensim in a fresh environment I get the following warning: Sorry it is a lot of output. The command is pip3 install gensim--4.2.0 my pip version is 22.3.1 and python version 3.11 (see below)

because of character limits, i have cut out chunks of the output to just the warnings:

      ...
      running egg_info
      writing gensim.egg-info/PKG-INFO
      writing dependency_links to gensim.egg-info/dependency_links.txt
      writing requirements to gensim.egg-info/requires.txt
      writing top-level names to gensim.egg-info/top_level.txt
      reading manifest file 'gensim.egg-info/SOURCES.txt'
      reading manifest template 'MANIFEST.in'
      warning: no files found matching 'COPYING.LESSER'
      warning: no files found matching 'ez_setup.py'
      warning: no files found matching 'gensim/models/doc2vec_inner.c'
      adding license file 'COPYING'
      writing manifest file 'gensim.egg-info/SOURCES.txt'
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.DTM' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.DTM' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.DTM' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.DTM' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.PathLineSentences' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.PathLineSentences' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.PathLineSentences' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.PathLineSentences' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.old_d2v_models' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.old_d2v_models' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.old_d2v_models' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.old_d2v_models' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.old_w2v_models' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.old_w2v_models' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.old_w2v_models' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.old_w2v_models' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      copying gensim/_matutils.c -> build/lib.macosx-10.9-universal2-cpython-311/gensim
      copying gensim/_matutils.pyx -> build/lib.macosx-10.9-universal2-cpython-311/gensim
      ...
      copying gensim/corpora/_mmreader.c -> build/lib.macosx-10.9-universal2-cpython-311/gensim/corpora
      copying gensim/corpora/_mmreader.pyx -> build/lib.macosx-10.9-universal2-cpython-311/gensim/corpora
      running build_ext
      building 'gensim.models.word2vec_inner' extension
      creating build/temp.macosx-10.9-universal2-cpython-311
      creating build/temp.macosx-10.9-universal2-cpython-311/gensim
      creating build/temp.macosx-10.9-universal2-cpython-311/gensim/models
      clang -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -arch arm64 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.11/include/python3.11 -I/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include -c gensim/models/word2vec_inner.c -o build/temp.macosx-10.9-universal2-cpython-311/gensim/models/word2vec_inner.o
      In file included from gensim/models/word2vec_inner.c:706:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/arrayobject.h:5:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/ndarrayobject.h:12:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/ndarraytypes.h:1948:
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: "Using deprecated NumPy API, disable it with "          "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-W#warnings]
      #warning "Using deprecated NumPy API, disable it with " \
       ^
      gensim/models/word2vec_inner.c:12424:5: error: incomplete definition of type 'struct _frame'
          __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      gensim/models/word2vec_inner.c:457:62: note: expanded from macro '__Pyx_PyFrame_SetLineNumber'
        #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
                                                            ~~~~~~~^
      /Library/Frameworks/Python.framework/Versions/3.11/include/python3.11/pytypedefs.h:22:16: note: forward declaration of 'struct _frame'
      typedef struct _frame PyFrameObject;
                     ^
      1 warning and 1 error generated.
      error: command '/usr/bin/clang' failed with exit code 1
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for gensim
  Running setup.py clean for gensim
Failed to build gensim
Installing collected packages: gensim
  Running setup.py install for gensim ... error
  error: subprocess-exited-with-error
  
  × Running setup.py install for gensim did not run successfully.
  │ exit code: 1
  ╰─> [540 lines of output]
      running install
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.
        warnings.warn(
      running build
      running build_py
      creating build
      creating build/lib.macosx-10.9-universal2-cpython-311
      creating build/lib.macosx-10.9-universal2-cpython-311/gensim
      ...
      copying gensim/corpora/svmlightcorpus.py -> build/lib.macosx-10.9-universal2-cpython-311/gensim/corpora
      copying gensim/corpora/hashdictionary.py -> build/lib.macosx-10.9-universal2-cpython-311/gensim/corpora
      running egg_info
      writing gensim.egg-info/PKG-INFO
      writing dependency_links to gensim.egg-info/dependency_links.txt
      writing requirements to gensim.egg-info/requires.txt
      writing top-level names to gensim.egg-info/top_level.txt
      reading manifest file 'gensim.egg-info/SOURCES.txt'
      reading manifest template 'MANIFEST.in'
      warning: no files found matching 'COPYING.LESSER'
      warning: no files found matching 'ez_setup.py'
      warning: no files found matching 'gensim/models/doc2vec_inner.c'
      adding license file 'COPYING'
      writing manifest file 'gensim.egg-info/SOURCES.txt'
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.DTM' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.DTM' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.DTM' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.DTM' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.PathLineSentences' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.PathLineSentences' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.PathLineSentences' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.PathLineSentences' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.old_d2v_models' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.old_d2v_models' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.old_d2v_models' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.old_d2v_models' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'gensim.test.test_data.old_w2v_models' as data is deprecated, please list it in `packages`.
          !!
      
      
          ############################
          # Package would be ignored #
          ############################
          Python recognizes 'gensim.test.test_data.old_w2v_models' as an importable package,
          but it is not listed in the `packages` configuration of setuptools.
      
          'gensim.test.test_data.old_w2v_models' has been automatically added to the distribution only
          because it may contain data files, but this behavior is likely to change
          in future versions of setuptools (and therefore is considered deprecated).
      
          Please make sure that 'gensim.test.test_data.old_w2v_models' is included as a package by using
          the `packages` configuration field or the proper discovery methods
          (for example by using `find_namespace_packages(...)`/`find_namespace:`
          instead of `find_packages(...)`/`find:`).
      
          You can read more about "package discovery" and "data files" on setuptools
          documentation page.
      
      
      !!
      
        check.warn(importable)
      copying gensim/_matutils.c -> build/lib.macosx-10.9-universal2-cpython-311/gensim
      copying gensim/_matutils.pyx -> build/lib.macosx-10.9-universal2-cpython-311/gensim
     ...
      copying gensim/corpora/_mmreader.pyx -> build/lib.macosx-10.9-universal2-cpython-311/gensim/corpora
      running build_ext
      building 'gensim.models.word2vec_inner' extension
      creating build/temp.macosx-10.9-universal2-cpython-311
      creating build/temp.macosx-10.9-universal2-cpython-311/gensim
      creating build/temp.macosx-10.9-universal2-cpython-311/gensim/models
      clang -Wsign-compare -Wunreachable-code -fno-common -dynamic -DNDEBUG -g -fwrapv -O3 -Wall -arch arm64 -arch x86_64 -g -I/Library/Frameworks/Python.framework/Versions/3.11/include/python3.11 -I/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include -c gensim/models/word2vec_inner.c -o build/temp.macosx-10.9-universal2-cpython-311/gensim/models/word2vec_inner.o
      In file included from gensim/models/word2vec_inner.c:706:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/arrayobject.h:5:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/ndarrayobject.h:12:
      In file included from /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/ndarraytypes.h:1948:
      /Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2: warning: "Using deprecated NumPy API, disable it with "          "#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION" [-W#warnings]
      #warning "Using deprecated NumPy API, disable it with " \
       ^
      gensim/models/word2vec_inner.c:12424:5: error: incomplete definition of type 'struct _frame'
          __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
          ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
      gensim/models/word2vec_inner.c:457:62: note: expanded from macro '__Pyx_PyFrame_SetLineNumber'
        #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
                                                            ~~~~~~~^
      /Library/Frameworks/Python.framework/Versions/3.11/include/python3.11/pytypedefs.h:22:16: note: forward declaration of 'struct _frame'
      typedef struct _frame PyFrameObject;
                     ^
      1 warning and 1 error generated.
      error: command '/usr/bin/clang' failed with exit code 1
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: legacy-install-failure

× Encountered error while trying to install package.
╰─> gensim

note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.

Versions

Please provide the output of:

>>> import platform; print(platform.platform())

import sys; print("Python", sys.version)

import struct; print("Bits", 8 * struct.calcsize("P"))

import numpy; print("NumPy", numpy.__version__)

import scipy; print("SciPy", scipy.__version__)
macOS-12.5-arm64-arm-64bit
>>> 
>>> import sys; print("Python", sys.version)
Python 3.11.1 (v3.11.1:a7a450f84a, Dec  6 2022, 15:24:06) [Clang 13.0.0 (clang-1300.0.29.30)]
>>> 
>>> import struct; print("Bits", 8 * struct.calcsize("P"))
Bits 64
>>> 
>>> import numpy; print("NumPy", numpy.__version__)
NumPy 1.23.5
>>> 
>>> import scipy; print("SciPy", scipy.__version__)
SciPy 1.9.3

Problem description

When I use the shardsize parameter in the similarities.Similarity method, when querying the index the same parameter is not used, causing errors:

self._similarity_index = similarities.Similarity(MODELS_PATH + f'/{model}', sim_vectors, num_features=len(self._dictionary), shardsize=50000)

sims = self._similarity_index[doc_vector]

PS: If I don't use the parameter shardsize, the error already occurs in the similarities.Similarity call.

Steps/code/corpus to reproduce

Save the .py files in the pruvo folder (package), the .parquet file in data folder and run this script:

import pandas as pd

from pruvo.embedding import Corpus

df = pd.read_parquet('data/preprocess.parquet')

corpus = Corpus()
corpus.add(list(df['bookingRoomType'].unique()), pre_processed=True)
corpus.add(list(df['mappedRoomType'].unique()), pre_processed=True)

w2v = corpus.train(model='word2vec')

w2v_similars = corpus.get_similars('apartment 1 king bed in neverland')
w2v_similars.head(10)

Versions

Please provide the output of:

import platform; print(platform.platform())
import sys; print("Python", sys.version)
import struct; print("Bits", 8 * struct.calcsize("P"))
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import gensim; print("gensim", gensim.__version__)
from gensim.models import word2vec;print("FAST_VERSION", word2vec.FAST_VERSION)

files.zip

from_topn will mark correct if the expected vector is not necessarily the most similar but among to from_topn most similar.

Useful for the evaluation of vectors like confusion vectors, in which any of the top two results match then it is marked correct.

Problem description

i have language with 240 distinct words. Because of it can fit 1 byte, i have map each word to bytes and save them in numpy uint8 array to minimize memory footprint. Doing this significantly reduce memory consumtion. However, due to "gensim\models\word2vec_inner.pyx", line 542, numpy arrays cant be used and throws: "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()" error

Related line checks if sentence is empty or not, however it doing it as "if not sent:" More generic checker if len(sent)==0: will fix the problem.

Work around is, casting numpy array to python list. However this significantly increase memory footprint and time consuming operation on big dataset.

What are you trying to achieve? What is the expected result? What are you seeing instead?

Steps/code/corpus to reproduce

reproduce:

class SentenceIterator:
    def __init__(self, dataset):
        self.dataset = dataset

    def __iter__(self):
        for sentence in self.dataset:
            yield sentence

data= []
data.append(np.array([22,33,44,55,1,2,3,5,4,100]))
data.append(np.array([100,100,100,100,11]))

sentences = SentenceIterator(data)
model = gensim.models.Word2Vec(sentences, vector_size=32, window=3, workers=4, sg=1, negative=10)

ps: casting np.array to python list fixes the issue however casting is very slow on big dataset and significantly increases memory footprint

**workaround:**
class SentenceIterator:
    def __init__(self, dataset):
        self.dataset = dataset

    def __iter__(self):
        for sentence in self.dataset:
            yield sentence.tolist()

possible fix

changing "if not sent:" controls to "if len(sent) ==0:"

Versions

Python 3.9.13 (main, Oct 13 2022, 21:23:06) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32 Type "help", "copyright", "credits" or "license" for more information.

import platform; print(platform.platform()) Windows-10-10.0.19044-SP0 import sys; print("Python", sys.version) Python 3.9.13 (main, Oct 13 2022, 21:23:06) [MSC v.1916 64 bit (AMD64)] import struct; print("Bits", 8 * struct.calcsize("P")) Bits 64 import numpy; print("NumPy", numpy.version) NumPy 1.23.4 import scipy; print("SciPy", scipy.version) SciPy 1.9.3 import gensim; print("gensim", gensim.version) gensim 4.2.0 from gensim.models import word2vec;print("FAST_VERSION", word2vec.FAST_VERSION) FAST_VERSION 0

• tox -e compile,docs fails with ERROR: tox config file (either pyproject.toml, tox.ini, setup.cfg) not found otherwise on my machine. tox -e ALL can be used now
• fixed napoleon import Could not import extension sphinxcontrib.napoleon (exception: cannot import name 'Callable' from 'collections' (/usr/lib/python3.10/collections/__init__.py)) https://github.com/sphinx-doc/sphinx/issues/10378#issuecomment-1107455569

Python 3.10.6, 5.15.65-1-MANJARO

Problem description

I have been trying to create a FastTextKeyedVectors and adding vectors to it using either add_vector or add_vectors but the methods are not adding anything. After looking at the implementation of those methods, I think there is an error while checking if a key has already been added.

Steps/code/corpus to reproduce

I create a FastTextKeyedVectors using the defaults used by the FastText model, then try to add vectors to it using add_vector or add_vectors:

wv = FastTextKeyedVectors(vector_size=2, min_n=3, max_n=6, bucket=2000000)
wv.add_vector("test", [0.5, 0.5])
print(wv.key_to_index)
>> {}
print(wv.index_to_key)
>> []
print(wv.vectors)
>> []

wv.add_vectors(["test"], [[0.5, 0.5]])
print(wv.key_to_index)
>> {}
print(wv.index_to_key)
>> []
print(wv.vectors)
>> []

wv.key_to_index, wv.index_to_key and wv.vectors are all empty.

FastTextKeyedVectors is a child of KeyedVectors where the add_vector/s methods are implemented. add_vector does a few checks then calls add_vectors. In add_vectors, there is an in_vocab_mask, which is a list of booleans indicating if a key is already present in the KeyedVectors.

in_vocab_mask = np.zeros(len(keys), dtype=bool)
        for idx, key in enumerate(keys):
            if key in self:
                in_vocab_mask[idx] = True

Since Gensim 4.0, key in wv will always return True with FastText by design. The proper way of checking if a key exists is by calling key in wv.key_to_index (See https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4#10-check-if-a-word-is-fully-oov-out-of-vocabulary-for-fasttext)

So replacing the above code by

in_vocab_mask = np.zeros(len(keys), dtype=bool)
        for idx, key in enumerate(keys):
            if key in self.key_to_index:
                in_vocab_mask[idx] = True

seems to fix the issue.

wv = FastTextKeyedVectors(vector_size=2, min_n=3, max_n=6, bucket=2000000)
wv.add_vectors(["test"], [[0.5, 0.5]])
print(wv.key_to_index)
>> {'test': 0}
print(wv.index_to_key)
>> ['test']
print(wv.vectors)
>> [[0.5 0.5]]

I am not sure how FastText models are able to add vectors to FastTextKeyedVectors the proper way when training without encountering this issue as I have not looked at the training code in detail.

Versions

Linux-5.10.0-17-amd64-x86_64-with-glibc2.31 Python 3.10.4 (main, Mar 31 2022, 08:41:55) [GCC 7.5.0] Bits 64 NumPy 1.21.6 SciPy 1.7.3 gensim 4.2.0 FAST_VERSION 1